Smart charging scheduling apparatus and method for electric vehicle

Abstract

The present disclosure provides a new and improved method and apparatus of scheduling for a charging infrastructure serving a plurality of electric vehicles. A computer-implemented method for scheduling a charging infrastructure serving a plurality of electric vehicles is provided, in which a prediction for a usage pattern of the charging infrastructure is made with a context based on historical usage patterns of the charging infrastructure and the contexts of the historical usage patterns, and a schedule scheme for deciding a distribution of charging spots of the charging infrastructure among the electric vehicles is determined based on the predicted usage pattern.

What is claimed is: 
     
       1. A computer-implemented method for scheduling a charging infrastructure serving a plurality of electric vehicles, comprising:
 making a prediction for a usage pattern of the charging infrastructure with a context based on historical usage patterns of the charging infrastructure and contexts of the historical usage patterns; 
 determining a schedule scheme for deciding a distribution of charging spots of the charging infrastructure among the electric vehicles based on the predicted usage pattern; and 
 deciding distribution of the charging spots among the electric vehicles based on the schedule scheme and information about the electric vehicles; 
 wherein the charging infrastructure comprises a plurality of supply phases for providing power to any of the charging spots, and the method further comprises: determining distribution of the supply phases among the charging spots based on at least one of: status of each supply phase and the information about the electric vehicles on the charging spots which are being powered by each charging supply. 
 
     
     
       2. The method of  claim 1 , wherein the prediction for the usage pattern of the charging infrastructure is made using a machine learning method or a data mapping method. 
     
     
       3. The method of  claim 1 , wherein the schedule scheme is determined based on a correspondence between the usage pattern and the schedule scheme. 
     
     
       4. The method of  claim 1 , further comprising: observing a real usage pattern of the charging infrastructure, wherein the predicted usage pattern is adjustable based on the real usage pattern. 
     
     
       5. The method of  claim 1 , wherein the schedule scheme comprises at least one of a first come first serve scheme, a round robin scheme, a shortest job first scheme, a shortest remaining time first scheme, a first priority first scheme, a first go first serve scheme, mixed schemes of any of these schedule schemes and transition schemes from one of the schedule schemes to another, and/or the usage pattern of the charging infrastructure comprises at least one of: a number of the electric vehicles demanding charging, a rate of change in the number of the electric vehicles, required power of the electric vehicle, and residence time of the electric vehicle, and/or the context of the usage pattern of the charging infrastructure comprises at least one of: a location of the charging infrastructure, a time of day, a day of week, weather, holiday or not, whether there is an event taking place around the charging infrastructure. 
     
     
       6. The method of  claim 1 , wherein the information about the electric vehicles comprises at least one of: a time at which the electric vehicle arrives at the charging infrastructure, a current state of charge, a desired state of charge, a desired charging energy, a desired pickup time, an accumulated charging time, remaining job length of the electric vehicle, priority of the electric vehicle, and the distance between the electric vehicle and the charging spot. 
     
     
       7. The method of  claim 1 , wherein the status of each supply phase comprises at least one of: an available power of the supply phase and a current of the supply phase. 
     
     
       8. The method of  claim 1 , further comprising: determining distribution of an available power of the charging phase among the charging spots based on power limitations of the supply phase and the information about the electric vehicles on the charging spots which are being powered by each charging supply. 
     
     
       9. The method of  claim 1 , wherein the electric vehicle is autonomous vehicle and the charging infrastructure has an autonomous charger. 
     
     
       10. A scheduling apparatus for a charging infrastructure serving a plurality of electric vehicles, comprising:
 a memory configured to store a series of computer executable instructions; and 
 a processor configured to execute the series of computer executable instructions, wherein the series of computer executable instructions, when executed by the processor, causes the processor to perform operations of: making a prediction for a usage pattern of the charging infrastructure with a context based on historical usage patterns of the charging infrastructure and the contexts of the historical usage patterns, and determining a schedule scheme for deciding a distribution of charging spots of the charging infrastructure among the electric vehicles based on the predicted usage pattern; 
 wherein the series of computer executable instructions, when executed by the processor, cause the processor to further perform operations of: deciding distribution of the charging spots among the electric vehicles based on the schedule scheme and information about the electric vehicles; and 
 wherein the charging infrastructure comprises a plurality of supply phases for providing power to any of the charging spots, and the series of computer executable instructions, when executed by the processor, causes the processor to further perform operations of: determining distribution of the supply phases among the charging spots based on at least one of: status of each supply phase and the information about the electric vehicles on the charging spots which are being powered by each charging supply. 
 
     
     
       11. The scheduling apparatus of  claim 10 , wherein the prediction for the usage pattern of the charging infrastructure is made using a machine learning method or a data mapping method. 
     
     
       12. The scheduling apparatus of  claim 10 , wherein the schedule scheme is determined based on a correspondence between the usage pattern and the schedule scheme. 
     
     
       13. The scheduling apparatus of  claim 10 , wherein the series of computer executable instructions, when executed by the processor, causes the processor to further perform operations of: observing a real usage pattern of the charging infrastructure, wherein the predicted usage pattern is adjustable based on the real usage pattern. 
     
     
       14. The scheduling apparatus of  claim 10 , wherein the schedule scheme comprises at least one of a first come first serve scheme, a round robin scheme, a shortest job first scheme, a shortest remaining time first scheme, a first priority first scheme, a first go first serve scheme, a mixed scheme of any of these schedule schemes and transition schemes from one of the schedule schemes to another, and/or the usage pattern of the charging infrastructure comprises at least one of: a number of the electric vehicles demanding charging, a rate of change in the number of the electric vehicles, required power of the electric vehicle, and residence time of the electric vehicle, and/or the context of the usage pattern of the charging infrastructure comprises at least one of: a location of the charging infrastructure, a time of day, a day of week, weather, holiday or not, whether there is an event taking place around the charging infrastructure. 
     
     
       15. The scheduling apparatus of  claim 10 , wherein the information about the electric vehicles comprises at least one of: a time at which the electric vehicle arrives at the charging infrastructure, a current state of charge, a desired state of charge, a desired charging energy, a desired pickup time, an accumulated charging time, remaining job length of the electric vehicle, priority of the electric vehicle, and the distance between the electric vehicle and the charging spot. 
     
     
       16. The scheduling apparatus of  claim 10 , wherein the status of each supply phase comprises at least one of: an available power of the supply phase and a current of the supply phase. 
     
     
       17. The scheduling apparatus of  claim 10 , wherein the series of computer executable instructions, when executed by the processor, causes the processor to further perform operations of: determining distribution of an available power of the charging phase among the charging spots based on power limitations of the supply phase and the information about the electric vehicles on the charging spots which are being powered by each charging supply. 
     
     
       18. The scheduling apparatus of  claim 10 , wherein the electric vehicle is an autonomous vehicle and the charging infrastructure has an autonomous charger. 
     
     
       19. A non-transitory computer readable medium having instructions stored thereon that, when executed by a processor, causes the processor to perform a method for scheduling a charging infrastructure serving a plurality of electric vehicles comprising:
 making a prediction for a usage pattern of the charging infrastructure with a context based on historical usage patterns of the charging infrastructure and contexts of the historical usage patterns; and 
 determining a schedule scheme for deciding a distribution of charging spots of the charging infrastructure among the electric vehicles based on the predicted usage pattern; and 
 deciding distribution of the charging spots among the electric vehicles based on the schedule scheme and information about the electric vehicles; 
 wherein the charging infrastructure comprises a plurality of supply phases for providing power to any of the charging spots, and the method further comprises: determining distribution of the supply phases among the charging spots based on at least one of: status of each supply phase and the information about the electric vehicles on the charging spots which are being powered by each charging supply.